The behavioral significance of resting state network after stroke: A study via graph theory analysis with near-infrared spectroscopy

Abstract

Accurate assessment of motor abilities for stroke patients is the basis for optimizing rehabilitation strategy. Previous studies have shown that functional connectivity under resting state (RS) is associated with motor dysfunction after stroke. However, the utility of topological properties of RS as a potential indicator for motor deficits after stroke has been little explored and needs more validation. In this study, near-infrared spectroscopy was used to measure the hemodynamic signals at the frontal cortex under RS and investigate the association between the network topological characteristics via graph theory analysis and the motor function for stroke patients. Seventeen patients participated in this study. Our results showed that the frontal network presented the small-world properties under RS. The global efficiency of the network was positively correlated with the Fugl-Meyer assessment (FMA) for patients. Meanwhile, there was a trend that the characteristic path length of the network positively correlated with patients’ FMA. Our findings suggest that, for patients with mild motor dysfunction, the enhanced connection between distant frontal areas may serve as a compensate mechanism to ensure the global efficiency across the network. This study demonstrates that topological properties of the frontal network under RS can predict the motor function of stroke patients to some extent, and highlights the clinical practicability of RS in stroke. The clinical trial registration number is ChiCTR2000040137.

Keywords

• Stroke
• Near-infrared spectroscopy
• Motor disability
• Resting state
• Graph theory